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Found 5 papers, 4 papers with code
What If Ground Truth Is Subjective? Personalized Deep Neural Hate Speech Detection
This supports our overall observation that personalized models should always be considered in all subjective NLP tasks, including hate speech detection.
From Generalized Laughter to Personalized Chuckles: Unleashing the Power of Data Fusion in Subjective Humor Detection
It seems that concatenating personalized datasets, even with the cost of normalizing the range of annotations across all datasets, if combined with the personalized models, results in an enormous increase in the performance of humor detection.
Towards Model-Based Data Acquisition for Subjective Multi-Task NLP Problems
Data annotated by humans is a source of knowledge by describing the peculiarities of the problem and therefore fueling the decision process of the trained model.
PALS: Personalized Active Learning for Subjective Tasks in NLP
In this paper, we present novel Personalized Active Learning techniques for Subjective NLP tasks (PALS) to either reduce the cost of the annotation process or to boost the learning effect.
ChatGPT: Jack of all trades, master of none
Our comparison of its results with available State-of-the-Art (SOTA) solutions showed that the average loss in quality of the ChatGPT model was about 25% for zero-shot and few-shot evaluation.
